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| # Copyright (c) OpenMMLab. All rights reserved. | |
| import pytest | |
| import torch | |
| import torch.nn as nn | |
| from mmcv.cnn import ConvModule | |
| from mmcv.utils.parrots_wrapper import _BatchNorm | |
| from mmpose.models.backbones import ResNet, ResNetV1d | |
| from mmpose.models.backbones.resnet import (BasicBlock, Bottleneck, ResLayer, | |
| get_expansion) | |
| def is_block(modules): | |
| """Check if is ResNet building block.""" | |
| if isinstance(modules, (BasicBlock, Bottleneck)): | |
| return True | |
| return False | |
| def all_zeros(modules): | |
| """Check if the weight(and bias) is all zero.""" | |
| weight_zero = torch.equal(modules.weight.data, | |
| torch.zeros_like(modules.weight.data)) | |
| if hasattr(modules, 'bias'): | |
| bias_zero = torch.equal(modules.bias.data, | |
| torch.zeros_like(modules.bias.data)) | |
| else: | |
| bias_zero = True | |
| return weight_zero and bias_zero | |
| def check_norm_state(modules, train_state): | |
| """Check if norm layer is in correct train state.""" | |
| for mod in modules: | |
| if isinstance(mod, _BatchNorm): | |
| if mod.training != train_state: | |
| return False | |
| return True | |
| def test_get_expansion(): | |
| assert get_expansion(Bottleneck, 2) == 2 | |
| assert get_expansion(BasicBlock) == 1 | |
| assert get_expansion(Bottleneck) == 4 | |
| class MyResBlock(nn.Module): | |
| expansion = 8 | |
| assert get_expansion(MyResBlock) == 8 | |
| # expansion must be an integer or None | |
| with pytest.raises(TypeError): | |
| get_expansion(Bottleneck, '0') | |
| # expansion is not specified and cannot be inferred | |
| with pytest.raises(TypeError): | |
| class SomeModule(nn.Module): | |
| pass | |
| get_expansion(SomeModule) | |
| def test_basic_block(): | |
| # expansion must be 1 | |
| with pytest.raises(AssertionError): | |
| BasicBlock(64, 64, expansion=2) | |
| # BasicBlock with stride 1, out_channels == in_channels | |
| block = BasicBlock(64, 64) | |
| assert block.in_channels == 64 | |
| assert block.mid_channels == 64 | |
| assert block.out_channels == 64 | |
| assert block.conv1.in_channels == 64 | |
| assert block.conv1.out_channels == 64 | |
| assert block.conv1.kernel_size == (3, 3) | |
| assert block.conv1.stride == (1, 1) | |
| assert block.conv2.in_channels == 64 | |
| assert block.conv2.out_channels == 64 | |
| assert block.conv2.kernel_size == (3, 3) | |
| x = torch.randn(1, 64, 56, 56) | |
| x_out = block(x) | |
| assert x_out.shape == torch.Size([1, 64, 56, 56]) | |
| # BasicBlock with stride 1 and downsample | |
| downsample = nn.Sequential( | |
| nn.Conv2d(64, 128, kernel_size=1, bias=False), nn.BatchNorm2d(128)) | |
| block = BasicBlock(64, 128, downsample=downsample) | |
| assert block.in_channels == 64 | |
| assert block.mid_channels == 128 | |
| assert block.out_channels == 128 | |
| assert block.conv1.in_channels == 64 | |
| assert block.conv1.out_channels == 128 | |
| assert block.conv1.kernel_size == (3, 3) | |
| assert block.conv1.stride == (1, 1) | |
| assert block.conv2.in_channels == 128 | |
| assert block.conv2.out_channels == 128 | |
| assert block.conv2.kernel_size == (3, 3) | |
| x = torch.randn(1, 64, 56, 56) | |
| x_out = block(x) | |
| assert x_out.shape == torch.Size([1, 128, 56, 56]) | |
| # BasicBlock with stride 2 and downsample | |
| downsample = nn.Sequential( | |
| nn.Conv2d(64, 128, kernel_size=1, stride=2, bias=False), | |
| nn.BatchNorm2d(128)) | |
| block = BasicBlock(64, 128, stride=2, downsample=downsample) | |
| assert block.in_channels == 64 | |
| assert block.mid_channels == 128 | |
| assert block.out_channels == 128 | |
| assert block.conv1.in_channels == 64 | |
| assert block.conv1.out_channels == 128 | |
| assert block.conv1.kernel_size == (3, 3) | |
| assert block.conv1.stride == (2, 2) | |
| assert block.conv2.in_channels == 128 | |
| assert block.conv2.out_channels == 128 | |
| assert block.conv2.kernel_size == (3, 3) | |
| x = torch.randn(1, 64, 56, 56) | |
| x_out = block(x) | |
| assert x_out.shape == torch.Size([1, 128, 28, 28]) | |
| # forward with checkpointing | |
| block = BasicBlock(64, 64, with_cp=True) | |
| assert block.with_cp | |
| x = torch.randn(1, 64, 56, 56, requires_grad=True) | |
| x_out = block(x) | |
| assert x_out.shape == torch.Size([1, 64, 56, 56]) | |
| def test_bottleneck(): | |
| # style must be in ['pytorch', 'caffe'] | |
| with pytest.raises(AssertionError): | |
| Bottleneck(64, 64, style='tensorflow') | |
| # expansion must be divisible by out_channels | |
| with pytest.raises(AssertionError): | |
| Bottleneck(64, 64, expansion=3) | |
| # Test Bottleneck style | |
| block = Bottleneck(64, 64, stride=2, style='pytorch') | |
| assert block.conv1.stride == (1, 1) | |
| assert block.conv2.stride == (2, 2) | |
| block = Bottleneck(64, 64, stride=2, style='caffe') | |
| assert block.conv1.stride == (2, 2) | |
| assert block.conv2.stride == (1, 1) | |
| # Bottleneck with stride 1 | |
| block = Bottleneck(64, 64, style='pytorch') | |
| assert block.in_channels == 64 | |
| assert block.mid_channels == 16 | |
| assert block.out_channels == 64 | |
| assert block.conv1.in_channels == 64 | |
| assert block.conv1.out_channels == 16 | |
| assert block.conv1.kernel_size == (1, 1) | |
| assert block.conv2.in_channels == 16 | |
| assert block.conv2.out_channels == 16 | |
| assert block.conv2.kernel_size == (3, 3) | |
| assert block.conv3.in_channels == 16 | |
| assert block.conv3.out_channels == 64 | |
| assert block.conv3.kernel_size == (1, 1) | |
| x = torch.randn(1, 64, 56, 56) | |
| x_out = block(x) | |
| assert x_out.shape == (1, 64, 56, 56) | |
| # Bottleneck with stride 1 and downsample | |
| downsample = nn.Sequential( | |
| nn.Conv2d(64, 128, kernel_size=1), nn.BatchNorm2d(128)) | |
| block = Bottleneck(64, 128, style='pytorch', downsample=downsample) | |
| assert block.in_channels == 64 | |
| assert block.mid_channels == 32 | |
| assert block.out_channels == 128 | |
| assert block.conv1.in_channels == 64 | |
| assert block.conv1.out_channels == 32 | |
| assert block.conv1.kernel_size == (1, 1) | |
| assert block.conv2.in_channels == 32 | |
| assert block.conv2.out_channels == 32 | |
| assert block.conv2.kernel_size == (3, 3) | |
| assert block.conv3.in_channels == 32 | |
| assert block.conv3.out_channels == 128 | |
| assert block.conv3.kernel_size == (1, 1) | |
| x = torch.randn(1, 64, 56, 56) | |
| x_out = block(x) | |
| assert x_out.shape == (1, 128, 56, 56) | |
| # Bottleneck with stride 2 and downsample | |
| downsample = nn.Sequential( | |
| nn.Conv2d(64, 128, kernel_size=1, stride=2), nn.BatchNorm2d(128)) | |
| block = Bottleneck( | |
| 64, 128, stride=2, style='pytorch', downsample=downsample) | |
| x = torch.randn(1, 64, 56, 56) | |
| x_out = block(x) | |
| assert x_out.shape == (1, 128, 28, 28) | |
| # Bottleneck with expansion 2 | |
| block = Bottleneck(64, 64, style='pytorch', expansion=2) | |
| assert block.in_channels == 64 | |
| assert block.mid_channels == 32 | |
| assert block.out_channels == 64 | |
| assert block.conv1.in_channels == 64 | |
| assert block.conv1.out_channels == 32 | |
| assert block.conv1.kernel_size == (1, 1) | |
| assert block.conv2.in_channels == 32 | |
| assert block.conv2.out_channels == 32 | |
| assert block.conv2.kernel_size == (3, 3) | |
| assert block.conv3.in_channels == 32 | |
| assert block.conv3.out_channels == 64 | |
| assert block.conv3.kernel_size == (1, 1) | |
| x = torch.randn(1, 64, 56, 56) | |
| x_out = block(x) | |
| assert x_out.shape == (1, 64, 56, 56) | |
| # Test Bottleneck with checkpointing | |
| block = Bottleneck(64, 64, with_cp=True) | |
| block.train() | |
| assert block.with_cp | |
| x = torch.randn(1, 64, 56, 56, requires_grad=True) | |
| x_out = block(x) | |
| assert x_out.shape == torch.Size([1, 64, 56, 56]) | |
| def test_basicblock_reslayer(): | |
| # 3 BasicBlock w/o downsample | |
| layer = ResLayer(BasicBlock, 3, 32, 32) | |
| assert len(layer) == 3 | |
| for i in range(3): | |
| assert layer[i].in_channels == 32 | |
| assert layer[i].out_channels == 32 | |
| assert layer[i].downsample is None | |
| x = torch.randn(1, 32, 56, 56) | |
| x_out = layer(x) | |
| assert x_out.shape == (1, 32, 56, 56) | |
| # 3 BasicBlock w/ stride 1 and downsample | |
| layer = ResLayer(BasicBlock, 3, 32, 64) | |
| assert len(layer) == 3 | |
| assert layer[0].in_channels == 32 | |
| assert layer[0].out_channels == 64 | |
| assert layer[0].downsample is not None and len(layer[0].downsample) == 2 | |
| assert isinstance(layer[0].downsample[0], nn.Conv2d) | |
| assert layer[0].downsample[0].stride == (1, 1) | |
| for i in range(1, 3): | |
| assert layer[i].in_channels == 64 | |
| assert layer[i].out_channels == 64 | |
| assert layer[i].downsample is None | |
| x = torch.randn(1, 32, 56, 56) | |
| x_out = layer(x) | |
| assert x_out.shape == (1, 64, 56, 56) | |
| # 3 BasicBlock w/ stride 2 and downsample | |
| layer = ResLayer(BasicBlock, 3, 32, 64, stride=2) | |
| assert len(layer) == 3 | |
| assert layer[0].in_channels == 32 | |
| assert layer[0].out_channels == 64 | |
| assert layer[0].stride == 2 | |
| assert layer[0].downsample is not None and len(layer[0].downsample) == 2 | |
| assert isinstance(layer[0].downsample[0], nn.Conv2d) | |
| assert layer[0].downsample[0].stride == (2, 2) | |
| for i in range(1, 3): | |
| assert layer[i].in_channels == 64 | |
| assert layer[i].out_channels == 64 | |
| assert layer[i].stride == 1 | |
| assert layer[i].downsample is None | |
| x = torch.randn(1, 32, 56, 56) | |
| x_out = layer(x) | |
| assert x_out.shape == (1, 64, 28, 28) | |
| # 3 BasicBlock w/ stride 2 and downsample with avg pool | |
| layer = ResLayer(BasicBlock, 3, 32, 64, stride=2, avg_down=True) | |
| assert len(layer) == 3 | |
| assert layer[0].in_channels == 32 | |
| assert layer[0].out_channels == 64 | |
| assert layer[0].stride == 2 | |
| assert layer[0].downsample is not None and len(layer[0].downsample) == 3 | |
| assert isinstance(layer[0].downsample[0], nn.AvgPool2d) | |
| assert layer[0].downsample[0].stride == 2 | |
| for i in range(1, 3): | |
| assert layer[i].in_channels == 64 | |
| assert layer[i].out_channels == 64 | |
| assert layer[i].stride == 1 | |
| assert layer[i].downsample is None | |
| x = torch.randn(1, 32, 56, 56) | |
| x_out = layer(x) | |
| assert x_out.shape == (1, 64, 28, 28) | |
| def test_bottleneck_reslayer(): | |
| # 3 Bottleneck w/o downsample | |
| layer = ResLayer(Bottleneck, 3, 32, 32) | |
| assert len(layer) == 3 | |
| for i in range(3): | |
| assert layer[i].in_channels == 32 | |
| assert layer[i].out_channels == 32 | |
| assert layer[i].downsample is None | |
| x = torch.randn(1, 32, 56, 56) | |
| x_out = layer(x) | |
| assert x_out.shape == (1, 32, 56, 56) | |
| # 3 Bottleneck w/ stride 1 and downsample | |
| layer = ResLayer(Bottleneck, 3, 32, 64) | |
| assert len(layer) == 3 | |
| assert layer[0].in_channels == 32 | |
| assert layer[0].out_channels == 64 | |
| assert layer[0].stride == 1 | |
| assert layer[0].conv1.out_channels == 16 | |
| assert layer[0].downsample is not None and len(layer[0].downsample) == 2 | |
| assert isinstance(layer[0].downsample[0], nn.Conv2d) | |
| assert layer[0].downsample[0].stride == (1, 1) | |
| for i in range(1, 3): | |
| assert layer[i].in_channels == 64 | |
| assert layer[i].out_channels == 64 | |
| assert layer[i].conv1.out_channels == 16 | |
| assert layer[i].stride == 1 | |
| assert layer[i].downsample is None | |
| x = torch.randn(1, 32, 56, 56) | |
| x_out = layer(x) | |
| assert x_out.shape == (1, 64, 56, 56) | |
| # 3 Bottleneck w/ stride 2 and downsample | |
| layer = ResLayer(Bottleneck, 3, 32, 64, stride=2) | |
| assert len(layer) == 3 | |
| assert layer[0].in_channels == 32 | |
| assert layer[0].out_channels == 64 | |
| assert layer[0].stride == 2 | |
| assert layer[0].conv1.out_channels == 16 | |
| assert layer[0].downsample is not None and len(layer[0].downsample) == 2 | |
| assert isinstance(layer[0].downsample[0], nn.Conv2d) | |
| assert layer[0].downsample[0].stride == (2, 2) | |
| for i in range(1, 3): | |
| assert layer[i].in_channels == 64 | |
| assert layer[i].out_channels == 64 | |
| assert layer[i].conv1.out_channels == 16 | |
| assert layer[i].stride == 1 | |
| assert layer[i].downsample is None | |
| x = torch.randn(1, 32, 56, 56) | |
| x_out = layer(x) | |
| assert x_out.shape == (1, 64, 28, 28) | |
| # 3 Bottleneck w/ stride 2 and downsample with avg pool | |
| layer = ResLayer(Bottleneck, 3, 32, 64, stride=2, avg_down=True) | |
| assert len(layer) == 3 | |
| assert layer[0].in_channels == 32 | |
| assert layer[0].out_channels == 64 | |
| assert layer[0].stride == 2 | |
| assert layer[0].conv1.out_channels == 16 | |
| assert layer[0].downsample is not None and len(layer[0].downsample) == 3 | |
| assert isinstance(layer[0].downsample[0], nn.AvgPool2d) | |
| assert layer[0].downsample[0].stride == 2 | |
| for i in range(1, 3): | |
| assert layer[i].in_channels == 64 | |
| assert layer[i].out_channels == 64 | |
| assert layer[i].conv1.out_channels == 16 | |
| assert layer[i].stride == 1 | |
| assert layer[i].downsample is None | |
| x = torch.randn(1, 32, 56, 56) | |
| x_out = layer(x) | |
| assert x_out.shape == (1, 64, 28, 28) | |
| # 3 Bottleneck with custom expansion | |
| layer = ResLayer(Bottleneck, 3, 32, 32, expansion=2) | |
| assert len(layer) == 3 | |
| for i in range(3): | |
| assert layer[i].in_channels == 32 | |
| assert layer[i].out_channels == 32 | |
| assert layer[i].stride == 1 | |
| assert layer[i].conv1.out_channels == 16 | |
| assert layer[i].downsample is None | |
| x = torch.randn(1, 32, 56, 56) | |
| x_out = layer(x) | |
| assert x_out.shape == (1, 32, 56, 56) | |
| def test_resnet(): | |
| """Test resnet backbone.""" | |
| with pytest.raises(KeyError): | |
| # ResNet depth should be in [18, 34, 50, 101, 152] | |
| ResNet(20) | |
| with pytest.raises(AssertionError): | |
| # In ResNet: 1 <= num_stages <= 4 | |
| ResNet(50, num_stages=0) | |
| with pytest.raises(AssertionError): | |
| # In ResNet: 1 <= num_stages <= 4 | |
| ResNet(50, num_stages=5) | |
| with pytest.raises(AssertionError): | |
| # len(strides) == len(dilations) == num_stages | |
| ResNet(50, strides=(1, ), dilations=(1, 1), num_stages=3) | |
| with pytest.raises(TypeError): | |
| # pretrained must be a string path | |
| model = ResNet(50) | |
| model.init_weights(pretrained=0) | |
| with pytest.raises(AssertionError): | |
| # Style must be in ['pytorch', 'caffe'] | |
| ResNet(50, style='tensorflow') | |
| # Test ResNet50 norm_eval=True | |
| model = ResNet(50, norm_eval=True) | |
| model.init_weights() | |
| model.train() | |
| assert check_norm_state(model.modules(), False) | |
| # Test ResNet50 with torchvision pretrained weight | |
| model = ResNet(depth=50, norm_eval=True) | |
| model.init_weights('torchvision://resnet50') | |
| model.train() | |
| assert check_norm_state(model.modules(), False) | |
| # Test ResNet50 with first stage frozen | |
| frozen_stages = 1 | |
| model = ResNet(50, frozen_stages=frozen_stages) | |
| model.init_weights() | |
| model.train() | |
| assert model.norm1.training is False | |
| for layer in [model.conv1, model.norm1]: | |
| for param in layer.parameters(): | |
| assert param.requires_grad is False | |
| for i in range(1, frozen_stages + 1): | |
| layer = getattr(model, f'layer{i}') | |
| for mod in layer.modules(): | |
| if isinstance(mod, _BatchNorm): | |
| assert mod.training is False | |
| for param in layer.parameters(): | |
| assert param.requires_grad is False | |
| # Test ResNet18 forward | |
| model = ResNet(18, out_indices=(0, 1, 2, 3)) | |
| model.init_weights() | |
| model.train() | |
| imgs = torch.randn(1, 3, 224, 224) | |
| feat = model(imgs) | |
| assert len(feat) == 4 | |
| assert feat[0].shape == (1, 64, 56, 56) | |
| assert feat[1].shape == (1, 128, 28, 28) | |
| assert feat[2].shape == (1, 256, 14, 14) | |
| assert feat[3].shape == (1, 512, 7, 7) | |
| # Test ResNet50 with BatchNorm forward | |
| model = ResNet(50, out_indices=(0, 1, 2, 3)) | |
| model.init_weights() | |
| model.train() | |
| imgs = torch.randn(1, 3, 224, 224) | |
| feat = model(imgs) | |
| assert len(feat) == 4 | |
| assert feat[0].shape == (1, 256, 56, 56) | |
| assert feat[1].shape == (1, 512, 28, 28) | |
| assert feat[2].shape == (1, 1024, 14, 14) | |
| assert feat[3].shape == (1, 2048, 7, 7) | |
| # Test ResNet50 with layers 1, 2, 3 out forward | |
| model = ResNet(50, out_indices=(0, 1, 2)) | |
| model.init_weights() | |
| model.train() | |
| imgs = torch.randn(1, 3, 224, 224) | |
| feat = model(imgs) | |
| assert len(feat) == 3 | |
| assert feat[0].shape == (1, 256, 56, 56) | |
| assert feat[1].shape == (1, 512, 28, 28) | |
| assert feat[2].shape == (1, 1024, 14, 14) | |
| # Test ResNet50 with layers 3 (top feature maps) out forward | |
| model = ResNet(50, out_indices=(3, )) | |
| model.init_weights() | |
| model.train() | |
| imgs = torch.randn(1, 3, 224, 224) | |
| feat = model(imgs) | |
| assert feat.shape == (1, 2048, 7, 7) | |
| # Test ResNet50 with checkpoint forward | |
| model = ResNet(50, out_indices=(0, 1, 2, 3), with_cp=True) | |
| for m in model.modules(): | |
| if is_block(m): | |
| assert m.with_cp | |
| model.init_weights() | |
| model.train() | |
| imgs = torch.randn(1, 3, 224, 224) | |
| feat = model(imgs) | |
| assert len(feat) == 4 | |
| assert feat[0].shape == (1, 256, 56, 56) | |
| assert feat[1].shape == (1, 512, 28, 28) | |
| assert feat[2].shape == (1, 1024, 14, 14) | |
| assert feat[3].shape == (1, 2048, 7, 7) | |
| # zero initialization of residual blocks | |
| model = ResNet(50, out_indices=(0, 1, 2, 3), zero_init_residual=True) | |
| model.init_weights() | |
| for m in model.modules(): | |
| if isinstance(m, Bottleneck): | |
| assert all_zeros(m.norm3) | |
| elif isinstance(m, BasicBlock): | |
| assert all_zeros(m.norm2) | |
| # non-zero initialization of residual blocks | |
| model = ResNet(50, out_indices=(0, 1, 2, 3), zero_init_residual=False) | |
| model.init_weights() | |
| for m in model.modules(): | |
| if isinstance(m, Bottleneck): | |
| assert not all_zeros(m.norm3) | |
| elif isinstance(m, BasicBlock): | |
| assert not all_zeros(m.norm2) | |
| def test_resnet_v1d(): | |
| model = ResNetV1d(depth=50, out_indices=(0, 1, 2, 3)) | |
| model.init_weights() | |
| model.train() | |
| assert len(model.stem) == 3 | |
| for i in range(3): | |
| assert isinstance(model.stem[i], ConvModule) | |
| imgs = torch.randn(1, 3, 224, 224) | |
| feat = model.stem(imgs) | |
| assert feat.shape == (1, 64, 112, 112) | |
| feat = model(imgs) | |
| assert len(feat) == 4 | |
| assert feat[0].shape == (1, 256, 56, 56) | |
| assert feat[1].shape == (1, 512, 28, 28) | |
| assert feat[2].shape == (1, 1024, 14, 14) | |
| assert feat[3].shape == (1, 2048, 7, 7) | |
| # Test ResNet50V1d with first stage frozen | |
| frozen_stages = 1 | |
| model = ResNetV1d(depth=50, frozen_stages=frozen_stages) | |
| assert len(model.stem) == 3 | |
| for i in range(3): | |
| assert isinstance(model.stem[i], ConvModule) | |
| model.init_weights() | |
| model.train() | |
| check_norm_state(model.stem, False) | |
| for param in model.stem.parameters(): | |
| assert param.requires_grad is False | |
| for i in range(1, frozen_stages + 1): | |
| layer = getattr(model, f'layer{i}') | |
| for mod in layer.modules(): | |
| if isinstance(mod, _BatchNorm): | |
| assert mod.training is False | |
| for param in layer.parameters(): | |
| assert param.requires_grad is False | |
| def test_resnet_half_channel(): | |
| model = ResNet(50, base_channels=32, out_indices=(0, 1, 2, 3)) | |
| model.init_weights() | |
| model.train() | |
| imgs = torch.randn(1, 3, 224, 224) | |
| feat = model(imgs) | |
| assert len(feat) == 4 | |
| assert feat[0].shape == (1, 128, 56, 56) | |
| assert feat[1].shape == (1, 256, 28, 28) | |
| assert feat[2].shape == (1, 512, 14, 14) | |
| assert feat[3].shape == (1, 1024, 7, 7) | |